Load all required libraries.

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.3     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(broom)

Read in raw data from RDS.

raw_data <- readRDS("./year2.RDS")

Make a few small modifications to names and data for visualizations.

final_data <- raw_data %>% mutate(log_copy_per_L = log10(mean_copy_num_L)) %>%
  rename(Facility = wrf) %>%
  mutate(Facility = recode(Facility, 
                           "NO" = "WRF A",
                           "MI" = "WRF B",
                           "CC" = "WRF C"))

Seperate the data by gene target to ease layering in the final plot

#make three data layers
only_positives <<- subset(final_data, (!is.na(final_data$Facility)))
only_n1 <- subset(only_positives, target == "N1")
only_n2 <- subset(only_positives, target == "N2")
only_background <<-final_data %>% 
  select(c(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke)) %>%
  group_by(date) %>% summarise_if(is.numeric, mean)

#specify fun colors
background_color <- "#7570B3"
seven_day_ave_color <- "#E6AB02"
marker_colors <- c("N1" = '#1B9E77',"N2" ='#D95F02')
#remove facilty C for now
#only_n1 <- only_n1[!(only_n1$Facility == "WRF C"),]
#only_n2 <- only_n2[!(only_n2$Facility == "WRF C"),]

only_n1 <- only_n1[!(only_n1$Facility == "WRF A" & only_n1$date == "2020-11-02"), ]
only_n2 <- only_n2[!(only_n2$Facility == "WRF A" & only_n2$date == "2020-11-02"), ]

Build the main plot

      #first layer is the background epidemic curve
        p1 <- only_background %>%
              plotly::plot_ly() %>%
              plotly::add_trace(x = ~date, y = ~new_cases_clarke, 
                                type = "bar", 
                                hoverinfo = "text",
                                text = ~paste('</br> Date: ', date,
                                                     '</br> Daily Cases: ', new_cases_clarke),
                                alpha = 0.5,
                                name = "Daily Reported Cases",
                                color = background_color,
                                colors = background_color,
                                showlegend = FALSE) %>%
            layout(yaxis = list(title = "Clarke County Daily Cases", showline=TRUE)) %>%
            layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
        
        #renders the main plot layer two as seven day moving average
        p1 <- p1 %>% plotly::add_trace(x = ~date, y = ~X7_day_ave_clarke, 
                             type = "scatter",
                             mode = "lines",
                             hoverinfo = "text",
                            text = ~paste('</br> Date: ', date,
                                                     '</br> Seven-Day Moving Average: ', X7_day_ave_clarke),
                             name = "Seven Day Moving Average Athens",
                             line = list(color = seven_day_ave_color),
                             showlegend = FALSE)
      

        
        #renders the main plot layer three as positive target hits
        
        p2 <- plotly::plot_ly() %>%
          plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
                                       type = "scatter",
                                       mode = "markers",
                                       hoverinfo = "text",
                                       text = ~paste('</br> Date: ', date,
                                                     '</br> Facility: ', Facility,
                                                     '</br> Target: ', target,
                                                     '</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
                                       data = only_n1,
                                       symbol = ~Facility,
                                       marker = list(color = '#1B9E77', size = 8, opacity = 0.65),
                                       showlegend = FALSE) %>%
          plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
                                       type = "scatter",
                                       mode = "markers",
                                       hoverinfo = "text",
                                       text = ~paste('</br> Date: ', date,
                                                     '</br> Facility: ', Facility,
                                                     '</br> Target: ', target,
                                                     '</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
                                       data = only_n2,
                                       symbol = ~Facility,
                                       marker = list(color = '#D95F02', size = 8, opacity = 0.65),
                                       showlegend = FALSE) %>%
            layout(yaxis = list(title = "SARS CoV-2 Copies/L", 
                                 showline = TRUE,
                                 type = "log",
                                 dtick = 1,
                                 automargin = TRUE)) %>%
            layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
        
        #adds the limit of detection dashed line
        p2 <- p2 %>% plotly::add_segments(x = as.Date("2021-06-30"), 
                                          xend = ~max(date + 10), 
                                          y = 3571.429, yend = 3571.429,
                                          opacity = 0.35,
                                          line = list(color = "black", dash = "dash")) %>%
          layout(annotations = list(x = as.Date("2021-06-30"), y = 3.8, xref = "x", yref = "y", 
                                    text = "Limit of Detection", showarrow = FALSE))

        

        p1
        p2

Combine the two main plot pieces as a subplot

#seperate n1 and n2 frames by site
#n1
wrf_a_only_n1 <- subset(only_n1, Facility == "WRF A")
wrf_b_only_n1 <- subset(only_n1, Facility == "WRF B")
wrf_c_only_n1 <- subset(only_n1, Facility == "WRF C")

#n2
wrf_a_only_n2 <- subset(only_n2, Facility == "WRF A")
wrf_b_only_n2 <- subset(only_n2, Facility == "WRF B")
wrf_c_only_n2 <- subset(only_n2, Facility == "WRF C")


#rejoin the old data frames then seperate in to averages for each plant. 
wrfa_both <- full_join(wrf_a_only_n1, wrf_a_only_n2)%>%
  select(c(date, mean_total_copies)) %>%
  group_by(date) %>%
  summarize_if(is.numeric, mean) %>%
  ungroup() %>%
  mutate(log_total_copies_both = log10(mean_total_copies))
## Joining, by = c("date", "new_cases_clarke", "cases_cum_clarke", "X7_day_ave_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")
wrfb_both <- full_join(wrf_b_only_n1, wrf_b_only_n2)%>%
  select(c(date, mean_total_copies)) %>%
  group_by(date) %>%
  summarize_if(is.numeric, mean) %>%
  ungroup() %>%
  mutate(log_total_copies_both = log10(mean_total_copies))
## Joining, by = c("date", "new_cases_clarke", "cases_cum_clarke", "X7_day_ave_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")
wrfc_both <- full_join(wrf_c_only_n1, wrf_c_only_n2)%>%
  select(c(date, mean_total_copies)) %>%
  group_by(date) %>%
  summarize_if(is.numeric, mean) %>%
  ungroup() %>%
  mutate(log_total_copies_both = log10(mean_total_copies))
## Joining, by = c("date", "new_cases_clarke", "cases_cum_clarke", "X7_day_ave_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")
#get max date
maxdate <- max(wrfa_both$date)
mindate <- min(wrfa_both$date)

Build loess smoothing figures figures

This makes the individual plots

#**************************************WRF A PLOT**********************************************
#add trendlines 
#extract data from geom_smooth
#both extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_botha <- ggplot(wrfa_both, aes(x = date, y = log_total_copies_both)) + 
  stat_smooth(aes(outfit=fit_botha<<-..y..), method = "loess", color = '#1B9E77', 
              span = 0.3, n = 113)
## Warning: Ignoring unknown aesthetics: outfit
#look at the fits to align dates and total observations
#both
extract_botha
## `geom_smooth()` using formula 'y ~ x'

fit_botha
##   [1] 11.56763 11.59345 11.62063 11.64904 11.67858 11.70910 11.74048 11.77311
##   [9] 11.80724 11.84256 11.87878 11.91561 11.95273 11.99822 12.04478 12.08318
##  [17] 12.11968 12.15416 12.18650 12.21660 12.23582 12.25500 12.28613 12.32050
##  [25] 12.35607 12.39083 12.42275 12.44222 12.46273 12.50164 12.55002 12.60105
##  [33] 12.64790 12.68374 12.71460 12.74171 12.75583 12.76073 12.76183 12.76459
##  [41] 12.77445 12.77167 12.77138 12.79399 12.82103 12.85201 12.88642 12.92377
##  [49] 12.99076 13.05858 13.10421 13.15582 13.20648 13.24927 13.27726 13.29588
##  [57] 13.30688 13.29809 13.27162 13.23645 13.20159 13.17600 13.15871 13.14170
##  [65] 13.12539 13.11053 13.09601 13.08075 13.06363 13.04357 13.01054 12.98976
##  [73] 12.97228 12.95506 12.93507 12.90926 12.88454 12.85657 12.81675 12.77134
##  [81] 12.72281 12.67361 12.62622 12.56389 12.50165 12.45217 12.39841 12.34594
##  [89] 12.30029 12.26703 12.26073 12.25894 12.25276 12.26041 12.27253 12.27977
##  [97] 12.27279 12.23615 12.19809 12.17559 12.15184 12.13219 12.12204 12.12674
## [105] 12.14251 12.16308 12.18839 12.21784 12.25233 12.29277 12.34003 12.39495
## [113] 12.45645
#assign fits to a vector
both_trenda <- fit_botha

#extract y min and max for each
limits_botha <- ggplot_build(extract_botha)$data
## `geom_smooth()` using formula 'y ~ x'
limits_botha <- as.data.frame(limits_botha)
both_ymina <- limits_botha$ymin
both_ymaxa <- limits_botha$ymax

#reassign dataframes (just to be safe)
work_botha <- wrfa_both

#fill in missing dates to smooth fits
work_botha <- work_botha %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_botha <- work_botha$date

#create a new smooth dataframe to layer
smooth_frame_botha <- data.frame(date_vec_botha, both_trenda, both_ymina, both_ymaxa)
#WRF A
#plot smooth frames
p_wrf_a <- plotly::plot_ly() %>%
  plotly::add_lines(x = ~date_vec_botha, y = ~both_trenda,
                    data = smooth_frame_botha,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_botha,
                                  '</br> Median Log Copies: ', round(both_trenda, digits = 2)),
                    line = list(color = '#1B9E77', size = 8, opacity = 0.65),
                    showlegend = FALSE) %>%
     layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_botha, ymin = ~both_ymina, ymax = ~both_ymaxa,
                    showlegend = FALSE,
                    opacity = 0.25,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_botha, #leaving in case we want to change
                                  '</br> Max Log Copies: ', round(both_ymaxa, digits = 2),
                                  '</br> Min Log Copies: ', round(both_ymina, digits = 2)),
                    name = "",
                    fillcolor = '#1B9E77',
                    line = list(color = '#1B9E77')) %>%
                layout(yaxis = list(title = "Total Log10 SARS CoV-2 Copies", 
                                 showline = TRUE,
                                 automargin = TRUE)) %>%
                layout(xaxis = list(title = "Date")) %>%
                layout(title = "WRF A") %>%
  plotly::add_markers(x = ~date, y = ~log_total_copies_both,
                      data = wrfa_both,
                       hoverinfo = "text",
                       showlegend = FALSE,
                       text = ~paste('</br> Date: ', date, 
                                     '</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
                       marker = list(color = '#1B9E77', size = 6, opacity = 0.65))

p_wrf_a
save(p_wrf_a, file = "./site_objects/wrf_a_year2.rda")
#**************************************WRF B PLOT**********************************************
#add trendlines 
#extract data from geom_smooth
#both extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_bothb <- ggplot(wrfb_both, aes(x = date, y = log_total_copies_both)) + 
  stat_smooth(aes(outfit=fit_bothb<<-..y..), method = "loess", color = '#D95F02', 
              span = 0.3, n = 113)
## Warning: Ignoring unknown aesthetics: outfit
#look at the fits to align dates and total observations
#both
extract_bothb
## `geom_smooth()` using formula 'y ~ x'

fit_bothb
##   [1] 10.69636 10.79791 10.89568 10.98983 11.08052 11.16790 11.25213 11.33221
##   [9] 11.40765 11.47940 11.54841 11.61561 11.68196 11.73909 11.79341 11.84720
##  [17] 11.89156 11.93287 11.97751 12.03186 12.09763 12.16616 12.24439 12.33833
##  [25] 12.43743 12.53116 12.60897 12.68536 12.75410 12.79807 12.83000 12.85305
##  [33] 12.87037 12.88511 12.88330 12.87699 12.87379 12.85814 12.83738 12.81888
##  [41] 12.80997 12.80342 12.80006 12.81056 12.83006 12.85486 12.88124 12.90546
##  [49] 12.93804 12.96965 12.98983 13.00937 13.02670 13.04025 13.04845 13.06592
##  [57] 13.08022 13.07112 13.05052 13.02444 12.99889 12.97990 12.96333 12.94721
##  [65] 12.93708 12.93083 12.92625 12.92113 12.91329 12.90050 12.87898 12.85201
##  [73] 12.81407 12.77482 12.74390 12.73098 12.74660 12.76839 12.79350 12.83714
##  [81] 12.88656 12.92901 12.95175 12.96870 12.97997 12.97016 12.94848 12.91842
##  [89] 12.88343 12.84699 12.79685 12.74305 12.69469 12.63958 12.58265 12.52883
##  [97] 12.48305 12.43754 12.39470 12.36132 12.33102 12.30407 12.28076 12.26135
## [105] 12.24519 12.23286 12.22510 12.22119 12.22114 12.22495 12.23262 12.24412
## [113] 12.25944
#assign fits to a vector
both_trendb <- fit_bothb

#extract y min and max for each
limits_bothb <- ggplot_build(extract_bothb)$data
## `geom_smooth()` using formula 'y ~ x'
limits_bothb <- as.data.frame(limits_bothb)
both_yminb <- limits_bothb$ymin
both_ymaxb <- limits_bothb$ymax

#reassign dataframes (just to be safe)
work_bothb <- wrfb_both

#fill in missing dates to smooth fits
work_bothb <- work_bothb %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_bothb <- work_bothb$date

#create a new smooth dataframe to layer
smooth_frame_bothb <- data.frame(date_vec_bothb, both_trendb, both_yminb, both_ymaxb)
#WRF B
#plot smooth frames
p_wrf_b <- plotly::plot_ly() %>%
  plotly::add_lines(x = ~date_vec_bothb, y = ~both_trendb,
                    data = smooth_frame_bothb,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_bothb,
                                  '</br> Median Log Copies: ', round(both_trendb, digits = 2)),
                    line = list(color = '#D95F02', size = 8, opacity = 0.65),
                    showlegend = FALSE) %>%
     layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_bothb, ymin = ~both_yminb, ymax = ~both_ymaxb,
                    showlegend = FALSE,
                    opacity = 0.25,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_bothb, #leaving in case we want to change
                                  '</br> Max Log Copies: ', round(both_ymaxb, digits = 2),
                                  '</br> Min Log Copies: ', round(both_yminb, digits = 2)),
                    name = "",
                    fillcolor = '#D95F02',
                    line = list(color = '#D95F02')) %>%
                layout(yaxis = list(title = "Total Log10 SARS CoV-2 Copies", 
                                 showline = TRUE,
                                 automargin = TRUE)) %>%
                layout(xaxis = list(title = "Date")) %>%
                layout(title = "WRF B") %>%
  plotly::add_markers(x = ~date, y = ~log_total_copies_both,
                      data = wrfb_both,
                       hoverinfo = "text",
                       showlegend = FALSE,
                       text = ~paste('</br> Date: ', date, 
                                     '</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
                       marker = list(color = '#D95F02', size = 6, opacity = 0.65))

p_wrf_b
save(p_wrf_b, file = "./site_objects/wrf_b_year2.rda")

#**************************************WRF C PLOT********************************************** #add trendlines #extract data from geom_smooth # *********************************span 0.6*********************************** #*****************Must always update the n = TOTAL NUMBER OF DAYS*************************

extract_bothc <- ggplot(wrfc_both, aes(x = date, y = log_total_copies_both)) + 
  stat_smooth(aes(outfit=fit_bothc<<-..y..), method = "loess", color = '#E7298A', 
              span = 0.3, n = 113)
## Warning: Ignoring unknown aesthetics: outfit
#look at the fits to align dates and total observations
#both
extract_bothc
## `geom_smooth()` using formula 'y ~ x'

fit_bothc
##   [1] 10.59484 10.71783 10.83411 10.94402 11.04789 11.14602 11.23875 11.32641
##   [9] 11.40891 11.48590 11.55706 11.62205 11.68052 11.72193 11.75651 11.80002
##  [17] 11.84611 11.89254 11.93707 11.97744 12.05207 12.12233 12.12555 12.09112
##  [25] 12.03942 11.99079 11.96557 11.92641 11.89432 11.91525 11.95769 12.01343
##  [33] 12.07429 12.13206 12.23058 12.32923 12.38899 12.45457 12.51670 12.56612
##  [41] 12.59357 12.57976 12.55788 12.53220 12.47494 12.40755 12.35145 12.32810
##  [49] 12.33278 12.34738 12.39009 12.46471 12.55104 12.62887 12.67798 12.73707
##  [57] 12.78984 12.79015 12.76851 12.73717 12.70843 12.69457 12.69229 12.69011
##  [65] 12.69136 12.69715 12.70414 12.70897 12.70827 12.69870 12.69007 12.66405
##  [73] 12.62738 12.58523 12.54278 12.50518 12.45709 12.41056 12.38342 12.36169
##  [81] 12.34398 12.32886 12.31494 12.30767 12.30180 12.29146 12.28248 12.27496
##  [89] 12.26900 12.26470 12.27122 12.27767 12.28259 12.29718 12.31046 12.31143
##  [97] 12.28911 12.23133 12.16603 12.10105 12.02132 11.94163 11.87679 11.84160
## [105] 11.82484 11.81443 11.81351 11.81754 11.82842 11.84809 11.87846 11.92161
## [113] 11.97539
#assign fits to a vector
both_trendc <- fit_bothc

#extract y min and max for each
limits_bothc <- ggplot_build(extract_bothc)$data
## `geom_smooth()` using formula 'y ~ x'
limits_bothc <- as.data.frame(limits_bothc)
both_yminc <- limits_bothc$ymin
both_ymaxc <- limits_bothc$ymax

#reassign dataframes (just to be safe)
work_bothc <- wrfc_both

#fill in missing dates to smooth fits
work_bothc <- work_bothc %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_bothc <- work_bothc$date

#create a new smooth dataframe to layer
smooth_frame_bothc <- data.frame(date_vec_bothc, both_trendc, both_yminc, both_ymaxc)
#WRF C
#plot smooth frames
p_wrf_c <- plotly::plot_ly() %>%
  plotly::add_lines(x = ~date_vec_bothc, y = ~both_trendc,
                    data = smooth_frame_bothc,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_bothc,
                                  '</br> Median Log Copies: ', round(both_trendc, digits = 2)),
                    line = list(color = '#E7298A', size = 8, opacity = 0.65),
                    showlegend = FALSE) %>%
     layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_bothc, ymin = ~both_yminc, ymax = ~both_ymaxc,
                    showlegend = FALSE,
                    opacity = 0.25,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_bothc, #leaving in case we want to change
                                  '</br> Max Log Copies: ', round(both_ymaxc, digits = 2),
                                  '</br> Min Log Copies: ', round(both_yminc, digits = 2)),
                    name = "",
                    fillcolor = '#E7298A',
                    line = list(color = '#E7298A')) %>%
                layout(yaxis = list(title = "Total Log10 SARS CoV-2 Copies", 
                                 showline = TRUE,
                                 automargin = TRUE)) %>%
                layout(xaxis = list(title = "Date")) %>%
                layout(title = "WRF C") %>%
  plotly::add_markers(x = ~date, y = ~log_total_copies_both,
                      data = wrfc_both,
                       hoverinfo = "text",
                       showlegend = FALSE,
                       text = ~paste('</br> Date: ', date, 
                                     '</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
                       marker = list(color = '#E7298A', size = 6, opacity = 0.65))

p_wrf_c
save(p_wrf_c, file = "./site_objects/wrf_c_year2.rda")

keeping in case

#save(wrfa_both, file = "./plotly_objs/wrfa_both.rda")
#save(wrfb_both, file = "./plotly_objs/wrfb_both.rda")
#save(wrfc_both, file = "./plotly_objs/wrfc_both.rda")
#save(date_vec_botha, file = "./plotly_objs/date_vec_botha.rda")
#save(date_vec_bothb, file = "./plotly_objs/date_vec_bothb.rda")
#save(date_vec_bothc, file = "./plotly_objs/date_vec_bothc.rda")
#save(both_ymina, file = "./plotly_objs/both_ymina.rda")
#save(both_ymaxa, file = "./plotly_objs/both_ymaxa.rda")

#save(both_yminb, file = "./plotly_objs/both_yminb.rda")
#save(both_ymaxb, file = "./plotly_objs/both_ymaxb.rda")

#save(both_yminc, file = "./plotly_objs/both_yminc.rda")
#save(both_ymaxc, file = "./plotly_objs/both_ymaxc.rda")